Abstract In today’s era of digital transformation, online transactions have become vital to financial systems, e-commerce, and decentralized applications. However, increasing dependence on digital payment infrastructures has also raised major security concerns such as hacking, identity theft, and unauthorized access. To address these challenges, the proposed project “Blockchain Secure Transaction” presents a decentralized framework that ensures transparency, integrity, and confidentiality in digital transactions. The system uses blockchain technology to record and validate each transaction in a distributed ledger, eliminating centralized control and making data immutable and tamper-proof. The workflow begins with user registration, where users provide details and set a picture password for secure recognition. During login, the system verifies credentials and performs biometric authentication to confirm user identity. Unregistered users are redirected to the registration page, maintaining process integrity. Once authenticated, users access the dashboard to initiate secure transactions. To preserve privacy, Zero-Knowledge Proof (ZKP) is used, allowing users to prove transaction authenticity without revealing sensitive information. Transactions then pass through smart contract verification, which ensures compliance with predefined conditions. Successful verifications result in completed transactions, while suspicious or invalid ones are blocked or frozen automatically. All user data and transaction logs are securely stored in Firebase, with backend processing handled in Java and the frontend designed using React (app.jsx). By combining blockchain’s immutability, smart contract automation, ZKP privacy proofs, and biometric authentication, the Blockchain Secure Transaction System offers a multi-layered, tamper-resistant, and transparent solution for secure online payments — enhancing trust and reliability in the digital economy.
The Absolute Smart Contract (ASC) presents a universal conceptual framework that unifies the spiritual, natural, and scientific dimensions of existence under one governing intelligence. It views reality — from atomic order to human morality — as operating within intrinsic laws of balance, reciprocity, and consequence. Whether expressed as divine will, natural order, or logical computation, each represents the same intelligent structure sustaining creation. The ASC is not a religion or ideology; it is a neutral interpretive model that reconciles seemingly divided worldviews through recognition of one absolute principle — the self-enforcing intelligence of existence itself.
Cryptocurrency markets are characterized by high volatility and complex patterns, creating both challenges and opportunities for traders and investors. This study introduces a machine learning framework for cryptocurrency trading optimization that leverages advanced analytical techniques to enhance trading decisions. We extracted historical data for 30 cryptocurrencies over a four-year period from Yahoo Finance. After preprocessing, we applied Principal Component Analysis (PCA) and K-means clustering to select representative coins. Four machine learning models (Gradient Boosting, XGBoost, Support Vector Regression, and Long Short-Term Memory networks) were trained to predict cryptocurrency price movements. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ). Gradient Boosting and XGBoost consistently outperformed SVR and LSTM models across all cryptocurrencies, with R 2 values of approximately 0.98 for most coins. The framework successfully identified trading signals through both moving average strategies and machine learning predictions, providing actionable insights for cryptocurrency traders. Our analysis demonstrates that ensemble-based models offer superior performance for cryptocurrency price prediction compared to neural network approaches. The integration of advanced visualization tools and trading signal generation creates a comprehensive system for data-driven cryptocurrency trading decisions.
This article explores the economic trade-offs between centralized and decentralized financial systems. Centralized finance (CeFi) relies on regulated intermediaries such as banks and custodians, offering stability, regulatory oversight, and support for monetary policy. Decentralized finance (DeFi), based on smart contracts and cryptographic protocols, reduces barriers to entry and increases flexibility but introduces technical and operational risks. The paper examines efficiency, risk allocation, financial inclusion, innovation, and international implications, supported by quantitative evidence such as global account ownership, cryptocurrency market capitalization, and total value locked (TVL) in DeFi. The analysis highlights that neither system is categorically superior; effective policy should balance innovation and stability through coordinated, technically informed, and proportionate regulations.
Nurhajar Anugraha, Muhammad Riswanto, Lindawati Lindawati, Asrul Asrul
Penelitian ini bertujuan untuk mengembangkan sistem e-voting berbasis blockchain dengan autentikasi biometrik sidik jari serta penerapan protokol zero-knowledge proofs sebagai pengamanan tambahan terhadap data pemilih dan hasil suara. Permasalahan utama yang dihadapi dalam sistem pemungutan suara elektronik konvensional adalah rendahnya kepercayaan terhadap keamanan data dan potensi manipulasi hasil. Metode penelitian yang digunakan mencakup perancangan sistem dengan arsitektur client-server, implementasi teknologi blockchain untuk pencatatan suara yang terenkripsi, serta integrasi biometrik sidik jari menggunakan BiometricPrompt API pada Android. Selain itu, sistem diverifikasi dengan kode OTP melalui email institusional sebagai bentuk validasi ganda pengguna. Hasil pengujian menunjukkan bahwa sistem dapat berjalan dengan baik dan memberikan keamanan yang tinggi karena setiap data suara tersimpan secara permanen dan tidak dapat diubah di jaringan blockchain. Autentikasi biometrik juga memastikan bahwa setiap pemilih terverifikasi secara unik sehingga tidak terjadi pemungutan suara ganda. Dengan demikian, sistem e-voting ini dinilai layak diterapkan untuk lingkungan akademik dan dapat dikembangkan lebih lanjut untuk pemilihan umum berskala lebih besar.
Kassem Danach, Abbas Tarhini, Wael Hosny Fouad Aly, Hussin Hejase
Blockchain technology relies on cryptographic mechanisms for transaction security and data integrity. However, the growing computational complexity, high transaction costs, and scalability issues pose significant challenges to blockchain adoption. Traditional cryptographic methods—such as hashing, key generation, encryption, and decryption—introduce excessive computational overhead, leading to energy inefficiencies and increased latency. This research proposes an optimization-driven crypto analysis framework that integrates metaheuristic algorithms, combinatorial optimization, reinforcement learning, and game theory to enhance the efficiency and security of blockchain cryptographic processes. The framework focuses on optimized cryptographic computation, gas fee reduction in smart contracts, security enhancement against cryptanalysis, and improved scalability of consensus mechanisms. Experimental evaluations demonstrate up to 39.4\% reduction in cryptographic execution time, 29.4\% savings in smart contract gas fees, and 33.3\% improvement in decentralization of Proof-of-Stake validators. These results validate the effectiveness of the proposed framework in achieving secure, scalable, and cost-efficient blockchain operations.
As the "agentic web" takes shape-billions of AI agents (often LLM-powered) autonomously transacting and collaborating-trust shifts from human oversight to protocol design. In 2025, several inter-agent protocols crystallized this shift, including Google's Agent-to-Agent (A2A), Agent Payments Protocol (AP2), and Ethereum's ERC-8004 "Trustless Agents," yet their underlying trust assumptions remain under-examined. This paper presents a comparative study of trust models in inter-agent protocol design: Brief (self- or third-party verifiable claims), Claim (self-proclaimed capabilities and identity, e.g. AgentCard), Proof (cryptographic verification, including zero-knowledge proofs and trusted execution environment attestations), Stake (bonded collateral with slashing and insurance), Reputation (crowd feedback and graph-based trust signals), and Constraint (sandboxing and capability bounding). For each, we analyze assumptions, attack surfaces, and design trade-offs, with particular emphasis on LLM-specific fragilities-prompt injection, sycophancy/nudge-susceptibility, hallucination, deception, and misalignment-that render purely reputational or claim-only approaches brittle. Our findings indicate no single mechanism suffices. We argue for trustless-by-default architectures anchored in Proof and Stake to gate high-impact actions, augmented by Brief for identity and discovery and Reputation overlays for flexibility and social signals. We comparatively evaluate A2A, AP2, ERC-8004 and related historical variations in academic research under metrics spanning security, privacy, latency/cost, and social robustness (Sybil/collusion/whitewashing resistance). We conclude with hybrid trust model recommendations that mitigate reputation gaming and misinformed LLM behavior, and we distill actionable design guidelines for safer, interoperable, and scalable agent economies.
Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attacks. Signature-based approaches of existing intrusion detection systems (IDS) do not cover the dynamically changing nature of cyber threats. Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis. Therefore, this research proposes integrating Ethereum Blockchain and Deep Learning to construct a well-founded security framework for cloud networks with data migration security and real-time intrusion detection. The architecture has five distinct methods, each of which deals with particular security issues. Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) guarantees tamper-proof and decentralized deep learning model training, which reduces model poisoning attacks by 98.4%. Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) captures graph structures for real-time anomaly detection in networks while reducing false positives to 1.2%. Quantum-inspired Variational Autoencoders (QI VAE ZDAD) provide enhanced zero-day attack detection, with an improved detection rate of 92%. Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) detects smart contract vulnerabilities automatically, resulting in an 87% reduction in fraud risk. Finally, Hierarchical Transformers for Secure Data Migration (HT SDM) enhance the transfer security of large-scale cloud data, achieving an attack classification accuracy of 99.1%. Overall, this multi-layer security framework will greatly enhance cloud security by preserving data integrity, cutting down the intrusion detection time by up to 65%, and enhancing response mechanisms. By marrying the immutable transparency of blockchain with superior anomaly detection at deep learning, this research provides a scalable, real-time, and intelligent approach to strengthening security against the backed-up transfer of data within cloud networks.
The rapid development of decentralized technologies and blockchain is transforming the methods of authentication, data management and the implementation of digital human rights, which actualizes the need to form a new identity paradigm based on user autonomy and trustful interaction without intermediaries. The purpose of this article is to substantiate self-sovereign identity as the foundation of trust and digital asset management within the Web3 ecosystem. The research methodology combines comparative legal and formal-dogmatic analysis, structural-functional modeling of the three-way interaction among issuer, holder, and verifier, as well as a problem-oriented review of the technical standards and practices of early platforms (Sovrin, uPort). It is demonstrated that the emergence of self-sovereign identity is a natural response to the shortcomings of centralized and federated identification models in Web 2.0 (OAuth 2.0, OpenID Connect): dependence on providers, concentration of leakage risks, and inability to disclose attributes selectively. The article reveals the mechanism of trust formation in the self-sovereign identity system, which is based on a three-party model of interaction between the issuer, the holder and the verifier; in this model, data authenticity is ensured using cryptographic verifiability through decentralized identifiers and verifiable credentials, which allows minimizing the participation of intermediaries, reducing the surface of possible attacks and guaranteeing the autonomy of the data subject in the process of managing their own digital identity. The key principles of self-sovereign identity (control, availability, transparency, minimization of disclosure, portability, security/resilience, and consent) are systematized, and their applied role in forming a «trust architecture» in Web3 (DAO, DeFi, NFT) is demonstrated. The study revealed a regulatory asymmetry between the technological development of self-sovereign identity systems and the level of their legal regulation. For Ukraine, key regulatory gaps have been specified that hinder the implementation of self-sovereign identity systems and limit the possibility of integrating Ukrainian e-government systems into the international Web3 space: the legislation lacks definitions of the terms «self-sovereign identity» and «decentralized identifier», which is why these concepts have no legal status in Ukraine; the current legal framework for electronic identification and personal data protection is incompatible with the principles of decentralization, self-control, and minimization of information disclosure, which underlie the SSI model. The practical significance of the results lies in the proposed holistic legal and technical framework for developing Web3 trust services, which enables the design of interoperable and secure processes for managing digital assets, prioritizing personal sovereignty over data.
Artur Iasenovets, Fei Tang, Huihui Zhu, Ping Wang · 5 authors
Permissioned blockchains ensure integrity and auditability of shared data but expose query parameters to peers during read operations, creating privacy risks for organizations querying sensitive records. This paper proposes a Private Information Retrieval (PIR) mechanism to enable private reads from Hyperledger Fabric's world state, allowing endorsing peers to process encrypted queries without learning which record is accessed. We implement and benchmark a PIR-enabled chaincode that performs ciphertext-plaintext (ct-pt) homomorphic multiplication directly within evaluate transactions, preserving Fabric's endorsement and audit semantics. The prototype achieves an average end-to-end latency of 113 ms and a peer-side execution time below 42 ms, with approximately 2 MB of peer network traffic per private read in development mode--reducible by half under in-process deployment. Storage profiling across three channel configurations shows near-linear growth: block size increases from 77 kilobytes to 294 kilobytes and world-state from 112 kilobytes to 332 kilobytes as the ring dimension scales from 8,192 to 32,768 coefficients. Parameter analysis further indicates that ring size and record length jointly constrain packing capacity, supporting up to 512 records of 64 bytes each under the largest configuration. These results confirm the practicality of PIR-based private reads in Fabric for smaller, sensitive datasets and highlight future directions to optimize performance and scalability.
The potential of agricultural data (AgData) to drive efficiency and sustainability is stifled by the "AgData Paradox": a pervasive lack of trust and interoperability that locks data in silos, despite its recognized value. This paper introduces AgriTrust, a federated semantic governance framework designed to resolve this paradox. AgriTrust integrates a multi-stakeholder governance model, built on pillars of Data Sovereignty, Transparent Data Contracts, Equitable Value Sharing, and Regulatory Compliance, with a semantic digital layer. This layer is realized through the AgriTrust Core Ontology, a formal OWL ontology that provides a shared vocabulary for tokenization, traceability, and certification, enabling true semantic interoperability across independent platforms. A key innovation is a blockchain-agnostic, multi-provider architecture that prevents vendor lock-in. The framework's viability is demonstrated through case studies across three critical Brazilian supply chains: coffee (for EUDR compliance), soy (for mass balance), and beef (for animal tracking). The results show that AgriTrust successfully enables verifiable provenance, automates compliance, and creates new revenue streams for data producers, thereby transforming data sharing from a trust-based dilemma into a governed, automated operation. This work provides a foundational blueprint for a more transparent, efficient, and equitable agricultural data economy.
Advanced blockchain technologies and growing environmental and economic uncertainties have Motivated us to investigate the impact of climate policy uncertainty (CPU) and global economic policy uncertainty (GEPU) on five green cryptocurrencies—ADA, EOS, IOTA, XLM, XTZ—selected based on energy efficiency and mining processes. We examined the short- and long-run impacts of alternative assets on these cryptocurrencies using a nonlinear autoregressive distributed lag model. In the long run, these cryptocurrencies are negatively affected by CPU and GEPU, questioning their safe-haven potential. In the short run, ADA, EOS, and XLM share a positive asymmetric relationship with CPU, whereas all cryptocurrencies have a negative asymmetric relationship with GEPU. Therefore, they can be considered a safe haven. In the short and long term, green bonds exert a positive impact, whereas interest rates, the S&P 500, and the gold index negatively impact these cryptocurrencies. In the short run, Bitcoin shows a negative relationship with EOS, IOTA, and XTZ and a positive relationship with ADA and XLM. Over the long term, Bitcoin exhibits a positive correlation with all cryptocurrencies. USD exhibits a positive relationship in the short run and a negative relationship in the long run with all cryptocurrencies. The findings offer practical implications for portfolio construction and investors dealing in the green cryptocurrency market.
In complex environments such as those incorporating distributed and edge computing, middleware plays a critical role in meeting the communication and performance requirements of distributed systems by providing communication flow and integration capabilities. Its inherent advantages, such as abstraction of complexities, enhanced interoperability and scalability, make it ideal for managing tasks such as federated learning in edge AI environments. In addition, by supporting secure and energy-efficient operations, the middleware fosters sustainability, enabling green blockchain solutions and low-power distributed ledger technologies (DLTs) to thrive for managing dynamic ecosystems such as dAIEDGE. This deliverable D5.3, "Middleware prototype" presents the first version of dAIEDGE middleware. This work has been developed during the first year of dAIEDGE project from M4 to M16. In general, the document outlines the first version of the middleware developed collaboratively with task partners, by the University of Salamanca (USAL) as part of Task T5.2, "Middleware and Networks for Edge AI," within the dAIEDGE project. This task reflects a joint effort involving multiple participants, including BCA, BTH, CETIC, KUL, VICOM, and UEDIN.
Abstract This research paper provides a comprehensive analysis of Bitcoin, the world’s preeminent cryptocurrency, focusing on the economic drivers of its price formation, its broader impact on the economy, and the evolving dynamics of its volatility. Drawing on high-frequency econometric modeling, time-series analysis, and network-based prediction methods, the paper synthesizes insights from leading empirical studies to elucidate the factors shaping Bitcoin’s price, including supply-demand fundamentals, investor behavior, macro-financial indicators, transaction network structure, and the influence of derivative markets. Additionally, it explores Bitcoin’s adoption in key industries, its intrinsic and extrinsic value determinants, and the implications of its volatility for financial stability. The study concludes by reflecting on the future trajectory of Bitcoin as it transitions from speculative asset to potential mainstream medium of exchange, considering regulatory, technological, and market challenges. Keywords: Bitcoin, cryptocurrency, price formation, volatility, supply-demand, GARCH, partial differential equations, transaction networks, futures markets, economic impact
ABSTRACT The Water Reserve Unit (WRU) proposes a new category of securitized, resource-backed reserve assets that integrate verified freshwater reserves into the global financial architecture.Unlike speculative digital assets, WRU represents a regulated and institutionally verified instrument designed to enhance global financial stability through linkage to real, measurable resources. The framework unites economic, legal, and technological dimensions — including distributed-ledger verification, sustainable-development principles, and international governance mechanisms — to enable transparent, compliant, and auditable issuance of water-backed value units.Technological transparency is achieved through distributed-ledger proof-of-reserve mechanisms ensuring real-time verification, accountability, and cross-border interoperability. Legally, the concept builds upon the United Nations General Assembly Resolution 64/292 (2010), which recognizes the human right to safe and clean drinking water and sanitation, and aligns with the UN Sustainable Development Goal 6 (Clean Water and Sanitation), embedding this right within a financial-institutional framework. By translating normative principles of international water law — such as those articulated in the 1997 UN Convention on the Law of the Non-Navigational Uses of International Watercourses — into measurable reserve instruments, WRU operationalizes the linkage between resource security and financial stability. Methodologically, the WRU framework is grounded in institutional economics (Commons, North), ecological macroeconomics, and sustainability finance, integrating valuation of natural capital with modern digital auditability. It provides a conceptual and technological foundation for recognizing water as a reserve-eligible asset, comparable in function to gold or Special Drawing Rights (SDRs), yet intrinsically tied to the planet’s most vital resource. Thus, WRU is not a cryptocurrency or utility token but a sovereignly regulated, resource-anchored financial standard — a new class of sustainability-linked reserve assets that integrate environmental resilience, economic equity, and technological trust within the evolving global financial system.
Public procurement in Africa is hindered by systemic corruption, inefficiency, and a lack of accountability, undermining economic growth and public trust. This analysis examines the transformative potential of smart contracts, built on Distributed Ledger Technology (DLT), as an innovative solution to enhance transparency and integrity in the continent's procurement systems. The study analyzes how smart contracts, by embedding procurement rules into immutable code, minimize human discretion and create tamper-proof audit trails for processes from bid submission to payment. Drawing on global precedents and emerging African cases (including DLT use in Guinea-Bissau's public wage bill), the paper finds that while smart contracts are technically feasible and highly beneficial, their successful adoption is contingent upon overcoming significant structural barriers. These challenges include adapting outdated legal frameworks to recognize the legal personality of contract code, addressing low digital infrastructure compatibility, and managing cultural resistance from officials who benefit from the existing discretionary systems. The paper concludes with key recommendations for African governments, emphasizing the necessity of parallel legal reform, targeted capacity building, and strong political commitment to leverage this technology for achieving Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions).
This article analyzes the impact of global financial technologies—specifically Blockchain, decentralized finance systems (DeFi), and Central Bank Digital Currencies (CBDC)—on the banking system within the IMRAD framework. The paper examines the transformational influence of modern FinTech innovations on traditional banking services, their role in expanding financial inclusion, and the associated issues of security and regulatory challenges. The study also highlights the prospects of implementing such technologies in developing countries like Uzbekistan.
A TANULMÁNY CÉLJAA tanulmány célja a Bitcoin buborékok kialakulásának vizsgálata, megértése. A buborékok erős hasonlóságot mutatnak a Gartner-féle hype-görbe alakjával, ezért az egyes buborékok és a hype-görbe kapcsolata is ismertetésre kerül. Ezek mellett a Bitcoin-buborékok kialakulását elősegítő tényezők feltárására törekedtünk. ALKALMAZOTT MÓDSZERTAN A Bitcoin árfolyamának historikus adatait elemeztük, melyek alapján a buborékok kirajzolódnak. A buborékok létezésének alátámasztására, illetve a Gartner-féle hype-görbével való azonosítás érdekében kiszámoltuk az egyes buborékok különböző időszakaihoz tartozó kockázatokat, hozamokat is. Illesztettük a hype-görbét a Bitcoin árfolyamának alakulására, illetve a korrelációs kapcsolatot is vizsgáltuk. LEGFONTOSABB EREDMÉNYEK A szórásból számított kockázatok, illetve a relatív szórások is alátámasztották a feltételezést, mely szerint az egyes Bitcoin-buborékok követik a Gartner-féle hype-görbe alakját. A görbék illesztése és a korreláció vizsgálata pedig kimutatta, hogy van kapcsolat a hype és az árfolyam alakulása között. A szabályozás szerepe kritikus lehet a kriptovaluták árfolyamának alakulásában, és a különböző országokban bevezetett szabályozó intézkedések jelentős hatást gyakorolhatnak a befektetői bizalomra és az árfolyamokra. A Bitcoin-bányászat felezése szintén fontos esemény, amely befolyásolhatja a kínálatot és keresletet és ennek megfelelően az árfolyamokat is. Az utánzó magatartás, vagyis a befektetők tendenciája arra, hogy mások viselkedését másolják, szintén jelentős tényező a buborékok kialakulásában. Végül az intézményi szereplők stabilizáló hatását ismertettük. GYAKORLATI JAVASLATOK A tanulmányból kiderül, hogy a fent említett tényezők igen nagy befolyást gyakorolnak a Bitcoin árfolyamának alakulására, melyek közül a bányászatért járó jutalmak felezése a leginkább szembetűnő, illetve számítással alátámasztható. Az új szabályozások megjelenésével nem tudunk számolni, viszont a felezéssel járó árfolyamváltozással igen, melynek fő indikátora az utánzó magatartás, hiszen a befektetők hozamaik maximalizálására törekednek. Ezek alapján a tanulmány rávilágít, hogy egy igen kockázatos befektetési formáról van szó, melynek előrejelzése igen nehéz feladat.
Éder Johnson de Area Leão Pereira, Thanmillys Nadhynne de Lima da Conceição, Emanuel Cruz Lima
The urgent need to mitigate climate change has elevated green hydrogen as a sustainable alternative to fossil fuels, while green cryptocurrencies have emerged to address the environmental concerns of traditional cryptocurrency mining. This study investigates the dynamic correlation between the green hydrogen market and selected green cryptocurrencies (Cardano, Stellar, Hedera, Algorand, and Chia) from July 2021 to April 2024, utilizing the Dynamic Conditional Correlation GARCH (DCC-GARCH) model with robustness checks using EGARCH and GJR-GARCH specifications. Our findings reveal significant correlations, with peaks reaching up to 50% in 2022, a period likely influenced by the Russia-Ukraine conflict. Subsequently, a decline in these correlations was observed in 2023. These results underscore the interconnectedness of sustainability-driven markets, suggesting potential contagion effects during periods of global instability. The high persistence of correlation shocks (α + β values approaching unity) indicates that correlation regimes tend to be long- lasting, with important implications for portfolio diversification and risk management strategies. Robustness checks using EGARCH and GJR-GARCH specifications confirmed qualitatively similar patterns, reinforcing the validity of our findings into the evolving landscape of green finance and energy.
This study adds a new dimension to the body of research by analyzing the impact of fiscal decentralization (FD) on ecological footprints (EF) in Pakistan. In Pakistan, the author examined how financing dependency (FD) affects economic efficiency (EE) from 1990 to 2022, considering time series data with the variables of renewable energy consumption (REC), nonrenewable energy consumption (NREC), GDP and trade openness (TOP). Based on the obtained data, the Auto Regressive Distributed Lag (ARDL) model is chosen. To promote environmental sustainability, the regression analysis reveals that NREC, GDP, and TOP improve EF in Pakistan, while FD and REC reduce EF. This study suggests that Pakistan should optimize the integration of strategies that improve ecological quality by providing the lower level of government with access to environmentally aware technological advancements. These findings could be considered as a policy recommendation.
Drone delivery services are encountering issues related to transparency, authenticity, and safeguarding privacy, highlighting the urgent need for an innovative approach that incorporates blockchain technology. This innovation aims to solidify the permanence of records, enable instantaneous verification, and streamline data handling in these intricate, self-operating transactions. In this paper, we use of blockchain for creating Non-Fungible Tokens (NFTs), which act as unalterable logs of purchase within the realm of delivery logistics. Our method adopts a distinctive two-fold strategy that places equal emphasis on both tangible goods and information. When integrating our solution with the Polygon network, we have achieved a substantial reduction in the costs associated with transactions while simultaneously enhancing the speed at which these transactions are processed. Our work not only addresses the existing challenges faced by unmanned aerial vehicle (UAV) communication systems but also sets a new standard for efficiency and security in the delivery logistics sector, paving the way for more reliable and transparent UAV-based delivery services.
Vivi Andersson, Sofia Bobadilla, Harald Hobbelhagen, Martin Monperrus
Smart contracts operate in a highly adversarial environment, where vulnerabilities can lead to substantial financial losses. Thus, smart contracts are subject to security audits. In auditing, proof-of-concept (PoC) exploits play a critical role by demonstrating to the stakeholders that the reported vulnerabilities are genuine, reproducible, and actionable. However, manually creating PoCs is time-consuming, error-prone, and often constrained by tight audit schedules. We introduce PoCo, an agentic framework that automatically generates executable PoC exploits from natural-language vulnerability descriptions written by auditors. PoCo autonomously generates PoC exploits in an agentic manner by interacting with a set of code-execution tools in a Reason–Act–Observe loop. It produces fully executable exploits compatible with the Foundry testing framework, ready for integration into audit reports and other security tools. We evaluate PoCo on a dataset of 23 real-world vulnerability reports. PoCo consistently outperforms the Zero-shot and Workflow baselines, generating well-formed and logically correct PoCs. Our results demonstrate that agentic frameworks can significantly reduce the effort required for high-quality PoCs in smart contract audits. Our contribution provides actionable knowledge for the smart contract security community.
With the introduction of blockchain technology and the emergence of non-fungible tokens (NFTs), users can prove ownership of digital content by cryptographically tokenizing the content they create, and it becomes possible to trade digital content. As user-generated digital content is frequently traded online, many scholars have analyzed the factors of user transactions, but there is a limitation that they have not been able to analyze the direct relationship between the sentiments of users and price. Therefore, this study uses multi-layer perceptron so as to analyze the factors that affect the price of profile picture (PFP) NFTs by using not only collectable market indicators and technical indicators but also sentiment indicators. As a result, it was found that PFP NFTs are closely correlated with various indicators, and a model was developed to accurately predict the price fluctuations of PFP NFTs using these indicators. The empirical results demonstrate that the proposed MLP model achieved prediction accuracies of 81.49% for BAYC and 93.39% for Cryptopunks. Furthermore, stock indices were found to exert a positive influence on NFT prices, whereas increases in cryptocurrency values, interest rates, and discussion volume acted as negative determinants. By contrast, the interaction of positive and objective sentiment contributed positively to price formation.